Rural children active trachoma risk factors and their interactions

Introduction Trachoma is a serious public health problem in rural Ethiopia. The aim of this investigation was to provide in-depth statistical analysis of the risk factors associated with active trachoma among children of age 1-9 years of Kedida Gamela district, in Ethiopia. Methods A community based cross-sectional survey of trachoma was conducted in six selected rural kebeles of Kedida Gamela district, in Ethiopia from June 10-25, 2014. A total of 377 children (ages 1-9 years) were included in the study using two stage cluster sampling. All children were examined for trachoma by nurse data collectors supervised by ophthalmic supervisors using the WHO simplified clinical grading system. Ordinal survey logistic regression model was used to identify risk factors. Data analysis was done using SAS version 9.3. Results The best fit proportional odds model was identified to be the main effects and two-way and three-way interactios. Keeping cattle in the house was found to have a protective effect (OR=0.138, p-value=0.0003). The household wealth will have a more protective effect if the child attends school. Washing face with soap and water once a day has equivalent protective effect as washing face three-or-more times a day with water only. Conclusion The 2-way and 3-way significant interactions effects unfolded some of the controversies derived from similar studies on trachoma risk factors. The findings would suggest integrated effort to address two or three factors simultaneously is more fruitful than any novel intervention targeted to address a single risk factor.


Introduction
Trachoma is the leading infectious cause of blindness worldwide. It is caused by infection with Chlamydia trachomatis and is characterized by inflammatory changes in the conjunctiva in children with subsequent scarring, corneal opacity and blindness in adults [1,2]. The World Health Organization established an alliance for the Global Elimination of Trachoma by the year 2020 (GET 2020). The main goal of the alliance was to ensure that trachoma will cease to be disease of the public health importance by the year 2020. The diagnosis of trachoma is made on clinical grounds. Laboratory testing is typically unavailable or unaffordable for clinical care in areas where trachoma is endemic. Different studies have shown that the highest incidence of trachoma is generally in the poorest countries with those which have no good sanitation, water facility, and relatively low levels of economic development [3,4]. Globally,  [9]. A number of descriptive and informative studies on trachoma were conducted in Ethiopia. These studies have greatly contributed to our understanding of many aspects of the trachoma problem and the magnitude of trachoma [3,[9][10][11][12][13][14][15]. They helped us to understand the complexity of not only the problem but also the need for sustained and coordinated interventions to effectively prevent and control eye problems related to trachoma infection. However, all of these studies focused more on descriptive statistical analyses. But advanced statistical analysis is useful to measure the intrinsic and explicit effects of the socio-economic, demographic and environmental factors on the risk of trachoma. This study is hence aimed to assess the associations between the potential risk factors and active trachoma for children aged 1-9 years in rural Ethiopia.
The study employed ordinal survey logistic regression analysis for in-depth investigation of the joint effect of two or more risk factors.

The data
We sought to conduct a community based cross-sectional survey of Kedida Gamela district was known for trachoma endemicity [15].
The study was undertaken among children aged 1-9 years old.
Structured questionnaire and physical examination were used to collect qualitative and quantitative primary data on sociodemographic and health characteristics. All information about a child was collected by interviewing the household head. Examination of child eyes was done by trained nurses. Two stage cluster sampling was employed to identify the study subjects. For the purpose of determining the sample size, we estimated the prevalence of trachoma for children aged 1-9 years old to be 37.00% [15]. We have also assumed a design effect of 4.2 (for cluster sampling), a confidence interval of 95% and a maximum allowable error of 10%.
These assumptions led to a sample size of 377 children. Out of 17 similarly populated Kebeles (the smallest administrative units in Ethiopia, like a ward or a county in some countries) in the district, six Kebeles were randomly selected. From each selected Kebeles all the children aged 1-9 years old were considered eligible for inclusion in the study. Before the start of the study, Kebele leaders were informed of the study and asked to assist in giving information and consent for examination. Trachoma testing was performed on consenting households.
The study was approved by the zonal health department. All enumerators were trained before initiation of the survey and a pilot test was undertaken in one of the neighboring Kebeles (not included in the final data) to test the procedures and the examiners. Six nurse data collectors and two ophthalmic supervisors with related work experience were recruited. The data collectors and supervisors were trained for two days on how to sample eligible children, administer the questionnaire, eye examination (anthropometric measurements) and address problems in the field. All the training Page number not for citation purposes 3 was delivered by the investigator and the two optometrists. All the 377 sampled children were examined for trachoma using the WHO simplified clinical grading system. Interviews and observations were used to assess risk factors. The child's face assessment was carried out before the trachoma examination for face cleanness, discharge and flies on the face. Children's eye examination was done using instrument.
The trachoma grader wearing 2.5x loupes and torch assessed each eye for the active trachoma using the WHO simplified grading scheme: Six trained examiners have to achieve at least 95% interobserver agreement in identifying active trachoma status. An ordinal severity score of active trachoma comprising three categories was then assigned to all eligible subjects on the bases of the worstaffected eye: 1=noTF, no TI (no active trachoma); 2=TF only(moderately active trachoma); and 3=TI (severely active trachoma) . Therefore, the response variable was measured by examining children's eyes using WHO standardized grading system.
The risk factors that were assumed to influence or predict the risk outcomes (active trachoma status), were comprised of socioeconomic, demographic, hygiene-sanitation and environmental variables that included gender, age, family size, educational level of the mother, monthly income of the family, face washing frequency,

Statistical model
Logistic regression is used to predict the probability of the risk outcomes on the basis of the risk factors. Moreover, the effect size of the risk factors on the risk outcomes can be determined from the logistic regression odds ratios. Ordinary logistic regression computes statistics under the assumption that the sample is drawn from a simple random sampling [16]. Survey logistic regression models have the same theory as ordinary logistic regression models. The difference between ordinary and survey logistic is that survey logistic accounts for the complexity of survey designs [17,18]. But, for data from simple random sampling, the survey logistic

Results
The data analysis for this study was done using SAS version 9.

Discussion
In this study the determinants of the risk factors of active trachoma in rural areas of Ethiopia were investigated using ordinal survey logistic regression. Keeping cattle in the same room as people overnight was found to be a significant protective factor. This protective effect may simply be due to a correlation between cattle ownership and wealth, which is in turn associated with better outcomes concerning trachoma. Bear in mind that the interaction effect of cattle keeping in the house and monthly income was not significant in this study (p=0.3467). However, other studies in Ethiopia [6,28], in South Sudan [8], and in Tanzania [29] showed otherwise. Their argument was that flies may breed on animal faeces which in turn increase exposure of children to flies. However, another study from Ethiopia [30] confirmed that neither cattle ownership nor the presence of cattle in the village has a major role in the size of the fly population; instead, the major determinant seems to be the way in which the cattle were kept. Thus, one of the future directions of this study will be to collect information on how the cattle were housed together with the family. Children from a family getting more income are more likely to have low level of active trachoma as compared to families having less monthly income. This may be because children from a relatively well to do family will have access to sanitary materials and a better information. Similar finding was reported in Ethiopia [3]. The result of our study showed that the effect of income will be prominent only if the child attends school. Facial hygiene has a positive effect on the reduction of the risk of trachoma. The effect of facial water, sanitation and hygiene (WASH) is enhanced by the daily frequency and application of soap in the WASH. The odds of low trachoma for children who wash their faces with water and soap once a day is equal to that of otherwise identical children who wash their faces three or more times with water only (Figure 2). Therefore it is advisable to wash children faces with water and soap at least once a day.
One of the most important interaction factors associated with low level of active trachoma was the use of pit toilet, where the child spends most of time and fly-density. This is a reasonable finding as using pit latrines may decrease the possibility of fly breeding in the area and hence the fly density. This association between low level of active trachoma and access to a latrine is consistent with previous studies [6,11,31]. However some other trachoma risk factor studies have reported that risk of trachoma was not associated with the use of pit latrine [28,32] Accordingly one may conclude that toilet facility has no association with the risk of trachoma outcomes [28,32]. Thus familiarity with interaction effects is essential in such a risk factor assessment.
Similarly, holistic interpretation of the joint effect of disposing garbage in a pit, where the child spends most of the time and flydensity may help to resolve the inconsistencies and controversies in similar studies. The use of pit instead of open field to dispose garbage is found to be significantly associated with low level of active trachoma in a study in Niger [31], but not supported in a community-based trachoma survey in the northern part of Ethiopia [33]. Again this anomaly might be lack of knowledge in the interaction effects. If one considers children who spent most of their time in the field (Figure 4), the conclusion is in line with the study in Niger [31] but if one considers children who spent most of the time at home the result is in line with another study in Ethiopia [33].

Conclusion
This article was aimed to make a contribution to the study of

What is known about this topic
• Several socioeconomic and lifestyle factors were found to independently affect the risk of trachoma (interactions between risk factors were not clearly indicated).
• The methods of analyses used do not take into account the complexity of survey techniques (the methods used assume simple random sampling even though in practice complex surveys were taken).

What this study adds
• In this study two stage cluster sampling was used to collect data and survey logistic regression model, which accounts for the complexity of survey designs and interaction effects, was used.

Competing interests
The authors declare no competing interests.

Authors' contributions
EKM and ZB conceived the study; ZB designed the questionnaire and collected the data. EKM revised the questionnaire. TZ designed and analyzed the data. TZ and EKM wrote the manuscript, and revised the manuscript. All authors read and approved the manuscript.
Page number not for citation purposes 7   Tables and figures   Table 1: Socio-economic, demographic and environmental effect on trachoma level